Abstract: Text classification is the key technology for processing large amount of text data. It can solve the information explosion problem in a certain extent. Random forests algorithm proposed by Breiman has the characteristics of good generalization and robustness, insensitivity for noise and ability in dealing with continuous attributes, which is very suitable for the establishment of text classification model. This paper attempted to construct the text classification model based on random forests algorithm, and compared with the text categorization model Reuters-21578 to verify the model's validity and accuracy for classification. Results showed: this model could be applied in text classification well; compared with the results of CART, REPTree and J48it models, it had the best effect, whose F1-Measure was 0.777; it had easy, intuitive and effective operation, and reliable results, which provided new idea for text classification research.

Key words: Random forests